Gastos Casa
24 February, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.9939e+08 2 5.0304 0.0067 **
## lag_depvar 2.6146e+11 1 2632.1066 <2e-16 ***
## Residuals 8.0562e+10 811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1928.568 16386.24 0.153104
## 2-0 31518.720 23270.685 39766.75 0.000000
## 2-1 24289.882 19507.024 29072.74 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
## 42 19319.29 1 30103.29
## 43 27926.29 1 19319.29
## 44 30715.43 1 27926.29
## 45 31962.29 1 30715.43
## 46 39790.14 1 31962.29
## 47 39211.57 1 39790.14
## 48 44548.57 1 39211.57
## 49 49398.00 1 44548.57
## 50 41039.00 1 49398.00
## 51 34821.29 1 41039.00
## 52 29123.57 1 34821.29
## 53 21275.71 1 29123.57
## 54 28476.14 1 21275.71
## 55 24561.86 1 28476.14
## 56 20323.57 1 24561.86
## 57 25370.00 1 20323.57
## 58 26811.86 1 25370.00
## 59 27151.86 1 26811.86
## 60 27623.29 1 27151.86
## 61 22896.57 1 27623.29
## 62 41889.29 1 22896.57
## 63 44000.14 1 41889.29
## 64 38558.00 1 44000.14
## 65 43373.86 1 38558.00
## 66 49001.00 1 43373.86
## 67 61213.29 1 49001.00
## 68 58939.57 1 61213.29
## 69 42046.86 1 58939.57
## 70 39191.71 1 42046.86
## 71 42646.43 1 39191.71
## 72 36121.57 1 42646.43
## 73 30915.57 1 36121.57
## 74 20273.43 1 30915.57
## 75 23938.29 1 20273.43
## 76 19274.29 1 23938.29
## 77 21662.29 1 19274.29
## 78 15819.00 1 21662.29
## 79 18126.14 1 15819.00
## 80 17240.71 1 18126.14
## 81 16127.71 1 17240.71
## 82 13917.14 1 16127.71
## 83 15379.86 1 13917.14
## 84 19510.14 1 15379.86
## 85 24567.29 1 19510.14
## 86 25700.43 1 24567.29
## 87 25729.00 1 25700.43
## 88 26435.00 1 25729.00
## 89 31157.14 1 26435.00
## 90 29818.43 1 31157.14
## 91 30962.43 1 29818.43
## 92 28746.71 1 30962.43
## 93 27830.71 1 28746.71
## 94 28252.14 1 27830.71
## 95 28717.57 1 28252.14
## 96 21365.43 1 28717.57
## 97 24816.86 1 21365.43
## 98 16838.57 1 24816.86
## 99 15529.14 1 16838.57
## 100 13286.29 1 15529.14
## 101 13629.43 1 13286.29
## 102 14404.86 1 13629.43
## 103 19524.86 1 14404.86
## 104 18475.71 1 19524.86
## 105 22495.00 1 18475.71
## 106 22254.57 1 22495.00
## 107 24173.29 1 22254.57
## 108 27466.43 1 24173.29
## 109 24602.43 1 27466.43
## 110 20531.14 1 24602.43
## 111 20846.43 1 20531.14
## 112 23875.71 1 20846.43
## 113 36312.71 1 23875.71
## 114 34244.00 1 36312.71
## 115 36347.43 1 34244.00
## 116 39779.71 1 36347.43
## 117 42018.71 1 39779.71
## 118 39372.57 1 42018.71
## 119 33444.00 1 39372.57
## 120 29255.86 1 33444.00
## 121 31640.14 1 29255.86
## 122 29671.14 1 31640.14
## 123 31023.71 1 29671.14
## 124 39723.43 1 31023.71
## 125 39314.14 1 39723.43
## 126 38239.86 1 39314.14
## 127 34649.43 1 38239.86
## 128 36688.43 1 34649.43
## 129 42867.57 1 36688.43
## 130 42226.86 1 42867.57
## 131 32155.14 1 42226.86
## 132 33603.00 1 32155.14
## 133 37254.43 1 33603.00
## 134 33145.57 1 37254.43
## 135 31299.43 1 33145.57
## 136 30252.00 1 31299.43
## 137 26310.71 1 30252.00
## 138 27929.86 1 26310.71
## 139 27666.14 1 27929.86
## 140 25017.57 1 27666.14
## 141 27335.00 1 25017.57
## 142 25760.71 1 27335.00
## 143 18436.86 1 25760.71
## 144 21906.00 1 18436.86
## 145 19418.14 1 21906.00
## 146 22826.14 1 19418.14
## 147 23444.29 1 22826.14
## 148 25264.86 1 23444.29
## 149 25473.29 1 25264.86
## 150 27366.86 1 25473.29
## 151 28855.86 1 27366.86
## 152 32326.86 1 28855.86
## 153 27141.43 1 32326.86
## 154 26297.71 1 27141.43
## 155 23499.14 1 26297.71
## 156 30246.29 1 23499.14
## 157 39931.86 1 30246.29
## 158 38020.43 2 39931.86
## 159 35004.00 2 38020.43
## 160 40750.86 2 35004.00
## 161 42363.29 2 40750.86
## 162 46273.57 2 42363.29
## 163 41083.29 2 46273.57
## 164 35711.29 2 41083.29
## 165 41921.71 2 35711.29
## 166 60583.29 2 41921.71
## 167 63115.57 2 60583.29
## 168 61300.14 2 63115.57
## 169 57666.43 2 61300.14
## 170 55834.00 2 57666.43
## 171 58927.71 2 55834.00
## 172 57810.57 2 58927.71
## 173 48987.14 2 57810.57
## 174 52219.29 2 48987.14
## 175 56503.57 2 52219.29
## 176 56545.00 2 56503.57
## 177 64705.57 2 56545.00
## 178 53833.29 2 64705.57
## 179 50114.00 2 53833.29
## 180 39592.43 2 50114.00
## 181 29907.29 2 39592.43
## 182 33923.29 2 29907.29
## 183 45489.00 2 33923.29
## 184 44866.29 2 45489.00
## 185 51680.57 2 44866.29
## 186 58257.00 2 51680.57
## 187 70600.57 2 58257.00
## 188 76648.00 2 70600.57
## 189 69430.14 2 76648.00
## 190 69651.57 2 69430.14
## 191 77745.14 2 69651.57
## 192 72795.86 2 77745.14
## 193 67670.71 2 72795.86
## 194 55357.86 2 67670.71
## 195 48524.00 2 55357.86
## 196 50154.43 2 48524.00
## 197 45111.57 2 50154.43
## 198 36147.00 2 45111.57
## 199 43501.57 2 36147.00
## 200 41472.43 2 43501.57
## 201 41058.00 2 41472.43
## 202 41605.57 2 41058.00
## 203 49382.86 2 41605.57
## 204 59558.57 2 49382.86
## 205 59134.57 2 59558.57
## 206 61109.00 2 59134.57
## 207 63004.43 2 61109.00
## 208 67344.29 2 63004.43
## 209 78180.86 2 67344.29
## 210 69117.86 2 78180.86
## 211 55597.57 2 69117.86
## 212 49426.14 2 55597.57
## 213 39119.43 2 49426.14
## 214 35636.86 2 39119.43
## 215 39201.14 2 35636.86
## 216 27777.00 2 39201.14
## 217 47207.00 2 27777.00
## 218 55587.29 2 47207.00
## 219 56619.71 2 55587.29
## 220 82679.86 2 56619.71
## 221 91259.57 2 82679.86
## 222 93552.71 2 91259.57
## 223 102242.71 2 93552.71
## 224 91884.00 2 102242.71
## 225 85013.86 2 91884.00
## 226 84535.29 2 85013.86
## 227 80700.43 2 84535.29
## 228 79740.57 2 80700.43
## 229 85163.14 2 79740.57
## 230 86724.86 2 85163.14
## 231 80355.00 2 86724.86
## 232 74875.14 2 80355.00
## 233 81347.00 2 74875.14
## 234 66062.43 2 81347.00
## 235 56946.43 2 66062.43
## 236 47732.14 2 56946.43
## 237 38129.71 2 47732.14
## 238 42928.29 2 38129.71
## 239 45392.57 2 42928.29
## 240 37895.43 2 45392.57
## 241 30660.29 2 37895.43
## 242 42430.86 2 30660.29
## 243 35845.14 2 42430.86
## 244 40350.43 2 35845.14
## 245 31494.71 2 40350.43
## 246 30013.29 2 31494.71
## 247 34197.57 2 30013.29
## 248 37430.14 2 34197.57
## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
## 254 46558.86 2 47139.71
## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 659 53752.98 22414.525
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2021.60487 4041.53695 -539.19334 2437.09935 -2971.84940 517.93999
## 8 9 10 11 12 13
## -5657.03349 -1186.35950 -3964.53529 -414.86347 -4937.04200 -1604.87459
## 14 15 16 17 18 19
## -895.22984 381.65156 -3239.61924 -373.67552 -2126.43286 6608.17809
## 20 21 22 23 24 25
## -1529.31245 -1207.91431 1476.27479 -1187.03241 234.59716 1694.59467
## 26 27 28 29 30 31
## -7103.47705 949.68507 8193.66815 415.17890 -16.95729 -2403.28700
## 32 33 34 35 36 37
## 1574.89909 4570.46565 1122.77258 2387.17155 -1873.00694 4604.37754
## 38 39 40 41 42 43
## 4301.66368 -2279.35325 -2983.48142 -1110.45055 -10741.17248 7294.90310
## 44 45 46 47 48 49
## 2558.44752 1366.59558 8104.25333 681.33318 6524.21168 6707.19214
## 50 51 52 53 54 55
## -5891.94403 -4800.90064 -5062.10733 -7928.12298 6134.14147 -4075.89584
## 56 57 58 59 60 61
## -4891.69492 3860.51224 889.98470 -30.71448 143.43240 -4995.47918
## 62 63 64 65 66 67
## 18130.07508 3634.50247 -3653.28500 5920.95273 7337.31286 14629.46177
## 68 69 70 71 72 73
## 1677.83901 -13226.83523 -1311.69993 4639.43107 -4906.08280 -4407.02242
## 74 75 76 77 78 79
## -10497.25805 2472.64076 -5395.75603 1070.24921 -6861.00317 555.26271
## 80 81 82 83 84 85
## -2347.43445 -2686.25298 -3923.66407 -528.11919 2323.23073 3769.02565
## 86 87 88 89 90 91
## 480.41571 -481.78527 199.23307 4304.07925 -1163.47794 1151.03747
## 92 93 94 95 96 97
## -2064.94225 -1043.61496 178.72544 275.67461 -7483.41939 2396.41336
## 98 99 100 101 102 103
## -8599.65619 -2933.20763 -4031.15556 -1726.95284 -1251.55394 3190.44397
## 104 105 106 107 108 109
## -2335.41133 2601.20073 -1153.52206 975.41285 2590.91272 -3152.47275
## 110 111 112 113 114 115
## -4719.59744 -844.55091 1909.06225 11697.38236 -1245.72111 2666.50414
## 116 117 118 119 120 121
## 4259.64046 3497.59434 -1106.23585 -4721.13137 -3725.58097 2320.64053
## 122 123 124 125 126 127
## -1733.07852 1341.10361 8858.18639 842.23677 125.81325 -2525.30512
## 128 129 130 131 132 133
## 2653.01446 7049.34156 1005.84491 -8505.65578 1748.48431 4133.96755
## 134 135 136 137 138 139
## -3167.54501 -1421.07617 -854.31515 -3879.77342 1185.46373 -493.96090
## 140 141 142 143 144 145
## -2911.95166 1721.27626 -1879.27143 -7826.63942 2046.17578 -3474.95382
## 146 147 148 149 150 151
## 2108.32372 -253.34514 1026.74820 -356.65424 1354.67602 1188.01688
## 152 153 154 155 156 157
## 3357.09797 -4863.22686 -1173.02072 -3233.88390 5960.21199 9746.36577
## 158 159 160 161 162 163
## -3664.96059 -5010.11649 3374.17880 -38.20257 2462.24349 -6147.03143
## 164 165 166 167 168 169
## -6980.86411 3926.61509 17158.04924 3373.44257 -656.11001 -2702.48999
## 170 171 172 173 174 175
## -1357.75177 3338.16091 -483.99547 -8330.64136 2616.33596 4074.57204
## 176 177 178 179 180 181
## 370.00156 8494.34959 -9513.19621 -3726.21304 -10995.79762 -11481.32140
## 182 183 184 185 186 187
## 1002.95976 9057.25273 -1678.03520 5680.72567 6299.02970 12892.44886
## 188 189 190 191 192 193
## 8147.17826 -4358.29582 2174.12315 10074.08676 -1951.87706 -2749.57286
## 194 195 196 197 198 199
## -10581.22086 -6649.23411 956.43144 -5512.00381 -10067.31326 5125.50200
## 200 201 202 203 204 205
## -3334.16847 -1974.39996 -1064.46964 6234.04267 9609.62577 288.40960
## 206 207 208 209 210 211
## 2633.56593 2802.63728 5485.21148 12527.19454 -6010.84729 -11606.82737
## 212 213 214 215 216 217
## -5956.68733 -10867.36430 -5338.17868 1271.12087 -13269.48335 16149.30619
## 218 219 220 221 222 223
## 7540.81793 1245.87749 26403.30827 12197.13081 6988.53271 13673.50514
## 224 225 226 227 228 229
## -4283.37928 -2096.29848 3432.09376 15.67933 2408.85982 8670.68992
## 230 231 232 233 234 235
## 5491.13605 -2244.21832 -2154.54055 9108.67297 -11834.61787 -7586.43212
## 236 237 238 239 240 241
## -8830.07123 -10375.91630 2818.61439 1087.23128 -8564.57926 -9244.53604
## 242 243 244 245 246 247
## 8852.13969 -8025.26669 2238.29032 -10556.65611 -4295.02095 1184.56354
## 248 249 250 251 252 253
## 758.57170 -12565.56694 3410.62103 1818.80710 3960.54768 1872.83026
## 254 255 256 257 258 259
## -1428.77885 10870.81247 20590.06252 2865.45915 -4606.85536 3784.76351
## 260 261 262 263 264 265
## -2024.54016 3413.48559 -5179.66574 -11209.32544 -5021.90532 -805.98603
## 266 267 268 269 270 271
## -5472.29640 8502.75265 -4575.56178 3901.55598 -2404.89606 4136.21957
## 272 273 274 275 276 277
## 404.06633 6996.06963 -1734.90025 11705.84299 -4930.22569 1390.86244
## 278 279 280 281 282 283
## -709.21532 7517.19620 -5407.68977 -3065.97661 -11586.46656 -2964.68162
## 284 285 286 287 288 289
## 18366.15780 7448.87755 2382.53161 -983.43528 556.57729 6050.03148
## 290 291 292 293 294 295
## 6521.61516 -19145.84999 -11455.74665 -8404.61122 9404.87957 2785.21609
## 296 297 298 299 300 301
## -1473.81178 27111.17980 9698.94695 4512.47772 9124.21847 2445.46591
## 302 303 304 305 306 307
## -1440.48320 7502.31032 -24701.64146 -3857.84160 -482.16122 -7270.21802
## 308 309 310 311 312 313
## -4249.21102 2668.71998 -9463.59573 -3471.92725 -8418.43798 1357.22529
## 314 315 316 317 318 319
## -3367.88580 1837.51246 -4304.70700 27231.11506 -1049.84183 2969.51778
## 320 321 322 323 324 325
## 10500.17546 5227.66149 32007.88055 4645.96760 -21398.22487 1417.36662
## 326 327 328 329 330 331
## 740.17942 -6828.79586 -2067.49735 -33588.25547 711.94123 -2475.83042
## 332 333 334 335 336 337
## -260.04307 -3336.65926 3924.82052 -616.15480 -7133.02491 -3275.55383
## 338 339 340 341 342 343
## -2344.25627 -7829.60959 3722.98538 -1522.05156 -1890.20678 -1146.32039
## 344 345 346 347 348 349
## 21.50194 319.65395 -1788.29252 -9616.29649 -13351.91104 2207.19163
## 350 351 352 353 354 355
## -4446.23695 -3775.48487 -6093.73263 1648.15821 1262.68668 2614.66053
## 356 357 358 359 360 361
## -3926.20137 -670.81825 515.73312 6841.01927 69.24083 -250.99732
## 362 363 364 365 366 367
## 2366.86520 -2979.43140 -1096.38835 -8959.85021 -4807.25633 -6377.87041
## 368 369 370 371 372 373
## -5094.16266 -7383.65257 4905.05686 228.41382 6966.34484 -7827.48391
## 374 375 376 377 378 379
## -2426.39720 -3547.48213 -2618.28942 -12604.91857 1799.58301 -10756.93001
## 380 381 382 383 384 385
## 5606.98048 9208.87924 2947.79600 -2597.52965 1410.70119 6537.73661
## 386 387 388 389 390 391
## 11170.38017 -6095.32610 -5632.88757 -406.52515 8312.88257 1525.57481
## 392 393 394 395 396 397
## 10925.28095 -10222.90069 2475.30568 403.38600 252.83051 -962.69174
## 398 399 400 401 402 403
## -865.89324 -14784.58927 8297.74901 -1440.22577 -1623.13909 6739.60103
## 404 405 406 407 408 409
## -8204.83651 -1528.88965 -2754.96742 -6028.64384 -3040.39275 -4086.43082
## 410 411 412 413 414 415
## -8909.33613 6014.64414 1484.43112 -7543.66784 -7833.81616 14107.28769
## 416 417 418 419 420 421
## 3623.02285 4272.74187 -8281.38772 -4952.42337 -2787.96526 2643.02578
## 422 423 424 425 426 427
## -14204.40936 -2922.54707 -9223.92469 2921.87300 6860.01762 6414.51395
## 428 429 430 431 432 433
## -4187.02701 -4305.11891 -4891.02362 -1941.28381 -5861.50886 -6756.68719
## 434 435 436 437 438 439
## -6057.82931 -1484.95176 -945.40037 -5080.82666 2487.17088 4720.37284
## 440 441 442 443 444 445
## -5208.92281 -2294.75648 1441.24169 -3988.04838 2695.04059 -6739.56255
## 446 447 448 449 450 451
## -12248.07427 -4602.64831 9562.28286 -2170.06543 4618.12905 -6034.18121
## 452 453 454 455 456 457
## -1265.85325 239.29663 2873.80925 -12439.81474 3247.04568 -6846.31831
## 458 459 460 461 462 463
## 6399.81745 2853.22054 2329.80802 -4037.26567 1916.04459 -196.27048
## 464 465 466 467 468 469
## 1602.00266 -721.85599 3151.02471 -2854.13284 5602.72468 -7169.31233
## 470 471 472 473 474 475
## -3157.76678 -2384.96936 -4834.18721 2844.70643 7628.96747 -6221.54642
## 476 477 478 479 480 481
## 1306.71412 -6363.87316 -3003.41804 1862.28326 -13091.95574 -9866.70138
## 482 483 484 485 486 487
## -1282.05912 -66.04217 -1059.59563 -1445.28610 -9692.07612 11017.82081
## 488 489 490 491 492 493
## 6100.10291 7252.78437 -5638.35557 5188.11411 9089.41676 5811.68880
## 494 495 496 497 498 499
## -13737.09882 -10766.63002 -3596.43100 -1249.50780 -667.30593 -7770.79580
## 500 501 502 503 504 505
## 494.06225 4161.96850 5359.65122 485.00620 -98.77571 -7419.44444
## 506 507 508 509 510 511
## 418.67601 -5205.01758 1693.78624 -1447.02758 -8306.54118 -721.42841
## 512 513 514 515 516 517
## -2797.88269 -706.52473 1209.07370 -9630.05712 -7867.46300 24206.40627
## 518 519 520 521 522 523
## 9636.12763 5649.48347 -5586.52778 2574.15894 16786.10784 11174.32640
## 524 525 526 527 528 529
## -24485.75952 -5284.76216 -3934.19375 4387.62636 -560.67265 -11304.24981
## 530 531 532 533 534 535
## 4236.10587 13733.09663 -5211.90060 4162.01829 5326.28365 -2039.42792
## 536 537 538 539 540 541
## -4777.93375 -7288.24720 -2283.01715 8148.65555 -81.72442 -8349.05073
## 542 543 544 545 546 547
## 1643.55183 -780.61723 187.16607 -11212.18106 -11198.59037 1943.83694
## 548 549 550 551 552 553
## 6889.61774 -1467.09461 688.98829 -7875.93635 8437.76763 738.83917
## 554 555 556 557 558 559
## -12118.13304 9034.63973 8485.79580 -111.42456 4642.80506 -3803.87183
## 560 561 562 563 564 565
## 13896.03468 21229.14367 -6818.78560 -9992.58717 6513.63134 -56.88812
## 566 567 568 569 570 571
## 3178.91176 -7659.26956 -17550.19825 6499.06848 6243.84524 1687.18585
## 572 573 574 575 576 577
## 2879.30525 1542.54517 -2394.67565 14501.15854 -9921.96447 -6471.11492
## 578 579 580 581 582 583
## 8513.77112 2626.82298 -6784.28579 7301.85222 -4034.82724 -2990.11474
## 584 585 586 587 588 589
## 15502.65928 -14760.97310 8231.49467 -158.60691 -6436.72048 -948.51356
## 590 591 592 593 594 595
## 62.97570 -10841.46425 1653.05069 -7297.82206 2941.15667 8723.25970
## 596 597 598 599 600 601
## -7682.31669 5699.79928 2557.35594 6667.06803 -3405.26492 5950.73255
## 602 603 604 605 606 607
## -8519.33625 2071.98414 1078.95532 2944.18668 1291.02615 190.73381
## 608 609 610 611 612 613
## -6015.03232 7896.79494 -1392.10369 -2773.40461 -3638.62760 -8397.38806
## 614 615 616 617 618 619
## 11822.61792 4751.84638 -9515.88546 11464.79092 5843.90117 -5797.46914
## 620 621 622 623 624 625
## 26163.74556 -13115.00546 -7005.06591 2965.52795 -4358.53552 -10771.14747
## 626 627 628 629 630 631
## 11159.65931 -21815.73813 -2514.15511 8579.02787 11001.93599 -1728.78737
## 632 633 634 635 636 637
## 33115.82890 -6873.83975 5468.23241 5139.76077 -2536.03575 -5592.10855
## 638 639 640 641 642 643
## -2157.56733 -12634.50554 -2396.25303 -2031.93453 -2659.92784 -2989.93630
## 644 645 646 647 648 649
## 1691.16208 4297.59395 16813.70220 18341.20635 610.80872 4524.64071
## 650 651 652 653 654 655
## 10341.34072 19855.10290 398.23520 -28388.47455 -1548.57709 -2484.50367
## 656 657 658 659 660 661
## 1691.02555 -3368.64357 -10781.24172 1544.13988 4100.35673 -1146.05883
## 662 663 664 665 666 667
## 12897.61527 1185.36357 1639.20225 -11866.01351 1245.28656 1050.39299
## 668 669 670 671 672 673
## -5304.84055 -7530.96314 1968.87963 -3817.47890 2577.30971 -3485.48453
## 674 675 676 677 678 679
## -9434.88656 -8381.16901 -3037.20269 112.15262 2777.53036 628.82996
## 680 681 682 683 684 685
## -3917.91927 -1893.63601 -1403.52939 -8329.12811 4579.01694 -2331.36647
## 686 687 688 689 690 691
## -1485.78944 498.96624 10759.11156 9723.01778 10472.25201 -9836.34944
## 692 693 694 695 696 697
## -3693.59156 -3266.63310 5753.82533 -10516.88627 -8013.87844 -8694.97411
## 698 699 700 701 702 703
## -6340.92522 -4797.08677 3027.86740 -4471.42435 -1963.45067 4154.86518
## 704 705 706 707 708 709
## 31023.74392 9388.41657 23310.88972 1534.60876 8189.51045 22791.48947
## 710 711 712 713 714 715
## 6425.76548 -18328.12239 4727.80944 -5534.95313 -182.14109 401.44362
## 716 717 718 719 720 721
## -17342.86510 -5324.75559 3278.41567 -3073.05330 -13035.63732 4232.57638
## 722 723 724 725 726 727
## -5608.61676 693.39257 -3985.99295 -12496.73329 1326.61497 -1911.89188
## 728 729 730 731 732 733
## -9823.11089 17229.10398 1718.36615 -2781.80324 5658.00167 -8692.98551
## 734 735 736 737 738 739
## -778.65603 8082.83148 -15414.40924 -5961.26035 7361.13816 -4839.63123
## 740 741 742 743 744 745
## 108.62304 1774.01317 -2012.21968 -5223.62151 6360.37152 -6333.77738
## 746 747 748 749 750 751
## 22644.58095 7754.23827 -2024.17277 -7361.58598 23350.55794 -4373.11881
## 752 753 754 755 756 757
## 1321.39794 -14495.46786 56048.50297 26826.95490 14994.73379 -10744.85763
## 758 759 760 761 762 763
## 10524.51047 7232.74167 5730.08140 -46465.63495 -16214.86281 932.20340
## 764 765 766 767 768 769
## -2553.07191 -3492.60071 122810.39424 19225.78250 43635.59712 22492.42558
## 770 771 772 773 774 775
## 11983.63620 15758.76976 25640.76217 -98897.75097 -6801.18741 -35871.90779
## 776 777 778 779 780 781
## 1711.19953 -1256.50943 3368.02331 -7444.79802 -1472.62909 -1973.03675
## 782 783 784 785 786 787
## 3396.75829 -7184.64489 -2263.79862 3892.69331 2236.73770 -2788.53916
## 788 789 790 791 792 793
## -4075.71494 1712.06898 2825.79616 -80.03229 -6731.80542 -5808.36928
## 794 795 796 797 798 799
## -1171.46134 -1287.47784 -7841.78356 -2376.28211 -3290.66845 -2687.84591
## 800 801 802 803 804 805
## 10702.00231 2214.15469 7051.66062 2892.93139 -5479.36450 8159.03321
## 806 807 808 809 810 811
## 9843.49367 -10636.25065 -7425.14494 -7530.48533 2983.51406 4170.31435
## 812 813 814 815 816
## -2295.33229 -14189.69461 -4129.72183 6241.15704 8215.26234
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17247.68 20097.46 24355.34 24073.04 26428.56 23758.77 24475.75 19703.50
## 10 11 12 13 14 15 16 17
## 19439.82 16780.15 17558.33 14284.73 14335.94 15001.21 16699.33 15017.82
## 18 19 20 21 22 23 24 25
## 16053.43 15426.39 22515.31 21598.49 21077.87 22969.60 22294.97 22948.12
## 26 27 28 29 30 31 32 33
## 24795.76 18718.60 20446.33 28290.82 28348.53 28021.14 25648.39 27052.11
## 34 35 36 37 38 39 40 41
## 30898.66 31247.40 32657.86 30166.19 34141.34 37352.35 34405.77 31213.74
## 42 43 44 45 46 47 48 49
## 30060.46 20631.38 28156.98 30595.69 31685.89 38530.24 38024.36 42690.81
## 50 51 52 53 54 55 56 57
## 46930.94 39622.19 34185.68 29203.84 22342.00 28637.75 25215.27 21509.49
## 58 59 60 61 62 63 64 65
## 25921.87 27182.57 27479.85 27892.05 23759.21 40365.64 42211.29 37452.90
## 66 67 68 69 70 71 72 73
## 41663.69 46583.82 57261.73 55273.69 40503.41 38007.00 41027.65 35322.59
## 74 75 76 77 78 79 80 81
## 30770.69 21465.64 24670.04 20592.04 22680.00 17570.88 19588.15 18813.97
## 82 83 84 85 86 87 88 89
## 17840.81 15907.98 17186.91 20798.26 25220.01 26210.79 26235.77 26853.06
## 90 91 92 93 94 95 96 97
## 30981.91 29811.39 30811.66 28874.33 28073.42 28441.90 28848.85 22420.44
## 98 99 100 101 102 103 104 105
## 25438.23 18462.35 17317.44 15356.38 15656.41 16334.41 20811.13 19893.80
## 106 107 108 109 110 111 112 113
## 23408.09 23197.87 24875.52 27754.90 25250.74 21690.98 21966.65 24615.33
## 114 115 116 117 118 119 120 121
## 35489.72 33680.92 35520.07 38521.12 40478.81 38165.13 32981.44 29319.50
## 122 123 124 125 126 127 128 129
## 31404.22 29682.61 30865.24 38471.91 38114.04 37174.73 34035.41 35818.23
## 130 131 132 133 134 135 136 137
## 41221.01 40660.80 31854.52 33120.46 36313.12 32720.50 31106.32 30190.49
## 138 139 140 141 142 143 144 145
## 26744.39 28160.10 27929.52 25613.72 27639.99 26263.50 19859.82 22893.10
## 146 147 148 149 150 151 152 153
## 20717.82 23697.63 24238.11 25829.94 26012.18 27667.84 28969.76 32004.66
## 154 155 156 157 158 159 160 161
## 27470.74 26733.03 24286.07 30185.49 41685.39 40014.12 37376.68 42401.49
## 162 163 164 165 166 167 168 169
## 43811.33 47230.32 42692.15 37995.10 43425.24 59742.13 61956.25 60368.92
## 170 171 172 173 174 175 176 177
## 57191.75 55589.55 58294.57 57317.78 49602.95 52429.00 56175.00 56211.22
## 178 179 180 181 182 183 184 185
## 63346.48 53840.21 50588.23 41388.61 32920.33 36431.75 46544.32 45999.85
## 186 187 188 189 190 191 192 193
## 51957.97 57708.12 68500.82 73788.44 67477.45 67671.06 74747.73 70420.29
## 194 195 196 197 198 199 200 201
## 65939.08 55173.23 49198.00 50623.58 46214.31 38376.07 44806.60 43032.40
## 202 203 204 205 206 207 208 209
## 42670.04 43148.81 49948.95 58846.16 58475.43 60201.79 61859.07 65653.66
## 210 211 212 213 214 215 216 217
## 75128.70 67204.40 55382.83 49986.79 40975.04 37930.02 41046.48 31057.69
## 218 219 220 221 222 223 224 225
## 48046.47 55373.84 56276.55 79062.44 86564.18 88569.21 96167.38 87110.16
## 226 227 228 229 230 231 232 233
## 81103.19 80684.75 77331.71 76492.45 81233.72 82599.22 77029.68 72238.33
## 234 235 236 237 238 239 240 241
## 77897.05 64532.86 56562.21 48505.63 40109.67 44305.34 46460.01 39904.82
## 242 243 244 245 246 247 248 249
## 33578.72 43870.41 38112.14 42051.37 34308.31 33013.01 36671.57 39498.00
## 250 251 252 253 254 255 256 257
## 30319.24 36262.62 40067.45 45266.88 47987.64 47479.76 57789.94 75302.83
## 258 259 260 261 262 263 264 265
## 75117.71 68422.38 69905.54 66122.94 67570.38 61322.47 50587.48 46611.27
## 266 267 268 269 270 271 272 273
## 46820.87 42924.10 51736.13 48005.87 52156.32 50271.21 54342.22 54638.50
## 274 275 276 277 278 279 280 281
## 60661.33 58293.44 67975.08 61894.42 62104.64 60452.23 66200.26 59925.12
## 282 283 284 285 286 287 288 289
## 56485.90 46028.82 44424.13 61671.84 67206.90 67616.72 65031.99 64118.54
## 290 291 292 293 294 295 296 297
## 68123.10 72036.85 53016.32 43109.47 37115.12 47445.78 50690.53 49803.68
## 298 299 300 301 302 303 304 305
## 74021.77 79972.52 80640.78 85257.39 83454.34 78480.12 81950.07 56826.27
## 306 307 308 309 310 311 312 313
## 53084.02 52763.50 46548.07 43754.99 47361.60 39907.07 38628.01 33184.63
## 314 315 316 317 318 319 320 321
## 36972.60 36153.20 39988.14 37970.74 63780.41 61619.63 63244.68 71250.05
## 322 323 324 325 326 327 328 329
## 73639.55 99144.32 97520.51 73328.78 72125.53 70481.37 62425.78 59545.40
## 330 331 332 333 334 335 336 337
## 29466.49 33157.40 33597.33 35919.37 35259.61 41031.87 42108.45 37351.70
## 338 339 340 341 342 343 344 345
## 36565.40 36692.18 32006.87 38011.34 38675.35 38934.03 39810.64 41598.20
## 346 347 348 349 350 351 352 353
## 43421.86 43173.30 36111.48 26670.67 32020.24 30880.20 30469.88 28084.13
## 354 355 356 357 358 359 360 361
## 32767.31 36525.05 40992.77 39180.10 40441.55 42581.98 49984.04 50535.14
## 362 363 364 365 366 367 368 369
## 50736.99 53202.43 50683.53 50127.56 42765.97 39960.16 36133.59 33910.22
## 370 371 372 373 374 375 376 377
## 29964.37 37259.01 39548.08 47440.91 41406.97 40853.62 39389.58 38921.92
## 378 379 380 381 382 383 384 385
## 29781.13 34383.50 27428.73 35655.69 45998.35 49567.10 47838.87 49832.41
## 386 387 388 389 390 391 392 393
## 56058.33 65552.61 58757.60 53220.67 52949.12 60335.57 60859.43 69536.19
## 394 395 396 397 398 399 400 401
## 58631.69 60200.04 59759.74 59243.12 57728.61 56489.02 43235.25 51828.94
## 402 403 404 405 406 407 408 409
## 50828.42 49793.68 56200.98 48736.46 48046.97 46372.07 42045.25 40874.86
## 410 411 412 413 414 415 416 417
## 38936.91 33025.50 40905.71 43834.81 38502.10 33585.71 48471.41 52319.83
## 418 419 420 421 422 423 424 425
## 56252.82 48714.85 45034.68 43709.40 47299.27 35707.40 35436.35 29689.70
## 426 427 428 429 430 431 432 433
## 35284.84 43620.34 50519.03 47281.40 44347.31 41269.57 41157.65 37632.12
## 434 435 436 437 438 439 440 441
## 33766.83 30998.24 32575.83 34426.97 32429.69 37300.48 43511.92 40261.19
## 442 443 444 445 446 447 448 449
## 39966.90 42976.19 40860.25 44853.56 40095.93 31119.65 29956.00 41323.78
## 450 451 452 453 454 455 456 457
## 41005.01 46661.61 42293.57 42643.56 44265.62 47987.39 37851.95 42705.89
## 458 459 460 461 462 463 464 465
## 38124.75 45701.07 49224.48 51847.55 48573.96 50916.98 51118.71 52867.43
## 466 467 468 469 470 471 472 473
## 52364.55 55311.13 52636.85 57692.88 50946.34 48554.97 47139.76 43760.86
## 474 475 476 477 478 479 480 481
## 47520.60 54991.12 49412.71 51117.59 45901.42 44278.86 47114.53 36518.56
## 482 483 484 485 486 487 488 489
## 30073.92 31945.04 34644.31 36135.71 37102.50 30737.18 43279.47 49946.07
## 490 491 492 493 494 495 496 497
## 56782.93 51489.31 56327.01 63968.03 67783.10 54026.20 44595.00 42618.08
## 498 499 500 501 502 503 504 505
## 42941.59 43733.51 38214.94 40616.17 45922.78 51609.85 52320.20 52430.87
## 506 507 508 509 510 511 512 513
## 46126.75 47468.02 43723.64 46481.74 46147.11 39856.86 40989.03 40163.38
## 514 515 516 517 518 519 520 521
## 41270.07 43912.63 36745.89 32020.74 55933.30 64101.80 67758.24 61130.98
## 522 523 524 525 526 527 528 529
## 62471.75 76070.39 83053.76 57980.05 52845.19 49536.37 53919.53 53425.39
## 530 531 532 533 534 535 536 537
## 43599.61 48596.19 61268.76 55784.41 59185.29 63176.86 60226.65 55252.68
## 538 539 540 541 542 543 544 545
## 48708.73 47363.34 55308.01 55058.19 47611.16 49836.90 49663.41 50357.90
## 546 547 548 549 550 551 552 553
## 40998.02 32826.02 37171.95 45296.24 45093.01 46800.51 40804.66 49826.16
## 554 555 556 557 558 559 560 561
## 50982.56 40752.07 50302.06 58172.28 57536.62 61137.73 56900.97 68672.57
## 562 563 564 565 566 567 568 569
## 85376.93 75458.59 64011.37 68434.75 66557.37 67745.13 59307.20 43281.22
## 570 571 572 573 574 575 576 577
## 50296.44 56207.10 57390.98 59468.45 60116.10 57239.84 69497.96 58861.40
## 578 579 580 581 582 583 584 585
## 52578.51 60187.18 61692.57 54780.15 61052.54 56624.54 53666.34 67249.12
## 586 587 588 589 590 591 592 593
## 52664.08 60015.18 59106.72 52823.08 52127.60 52403.89 43111.09 45910.54
## 594 595 596 597 598 599 600 601
## 40531.99 44781.74 53553.17 46878.20 52742.64 55122.65 60796.98 56951.55
## 602 603 604 605 606 607 608 609
## 61769.76 53330.59 55212.33 55989.38 58299.69 58874.27 58414.60 52586.63
## 610 611 612 613 614 615 616 617
## 59654.82 57713.12 54807.63 51510.67 44467.10 55988.01 59879.03 50806.07
## 618 619 620 621 622 623 624 625
## 61217.67 65406.47 58890.25 81138.29 66247.35 58569.61 60574.39 55923.43
## 626 627 628 629 630 631 632 633
## 46249.91 56967.17 37505.58 37365.69 46942.78 57435.07 55477.89 84233.27
## 634 635 636 637 638 639 640 641
## 74410.48 76613.24 78252.04 72973.54 65686.14 62317.36 50211.25 48578.08
## 642 643 644 645 646 647 648 649
## 47468.64 45949.51 44332.70 47011.98 51633.58 66618.08 81055.48 78176.22
## 650 651 652 653 654 655 656 657
## 79080.80 84957.61 98414.48 93168.33 63411.43 60860.93 57812.55 58798.07
## 658 659 660 661 662 663 664 665
## 55235.81 45639.86 48026.36 52348.06 51539.53 63111.78 62989.37 63279.16
## 666 667 668 669 670 671 672 673
## 51724.14 53084.89 54104.27 49438.82 43413.12 46450.76 44047.40 47537.34
## 674 675 676 677 678 679 680 681
## 45287.74 38118.88 32772.06 32769.56 35521.04 40257.31 42519.78 40522.49
## 682 683 684 685 686 687 688 689
## 40546.10 40995.27 35332.55 41667.65 41164.65 41464.18 43461.46 54178.84
## 690 691 692 693 694 695 696 697
## 62643.75 70700.21 59987.45 55991.63 52871.17 58029.89 48314.02 42007.40
## 698 699 700 701 702 703 704 705
## 35897.64 32613.80 31092.42 36604.00 34866.02 35539.28 41477.54 70162.73
## 706 707 708 709 710 711 712 713
## 76326.82 93889.68 90205.63 92803.22 107841.81 106681.41 84023.05 84370.67
## 714 715 716 717 718 719 720 721
## 75701.28 72801.41 70776.15 53490.47 48884.73 52379.91 49882.49 38988.00
## 722 723 724 725 726 727 728 729
## 44560.90 40828.89 43075.99 40949.30 31648.39 35602.61 36228.40 29858.32
## 730 731 732 733 734 735 736 737
## 47941.92 50191.52 48223.71 53882.56 46282.51 46557.31 54545.69 40985.40
## 738 739 740 741 742 743 744 745
## 37394.29 45902.92 42674.66 44178.56 46949.65 46062.05 42478.06 49472.92
## 746 747 748 749 750 751 752 753
## 44489.70 65470.05 70794.89 66900.87 58829.30 78625.26 71693.60 70611.90
## 754 755 756 757 758 759 760 761
## 55836.50 104598.19 121683.27 126276.14 107786.35 110216.69 109463.49 107491.06
## 762 763 764 765 766 767 768 769
## 60128.72 45167.08 47077.93 45701.31 43676.18 152339.50 156780.12 182005.72
## 770 771 772 773 774 775 776 777
## 185575.22 179507.80 177503.52 184391.47 81522.76 72104.05 38450.51 41886.37
## 778 779 780 781 782 783 784 785
## 42295.69 46697.08 41091.20 41411.47 41253.96 45811.36 40544.23 40241.45
## 786 787 788 789 790 791 792 793
## 45359.69 48386.97 46640.00 43987.07 46728.06 50098.46 50504.66 45043.80
## 794 795 796 797 798 799 800 801
## 41076.46 41661.91 42072.35 36700.42 36782.24 36054.27 35944.85 47556.70
## 802 803 804 805 806 807 808 809
## 50288.20 56906.21 59056.51 53616.25 60784.36 68524.68 57385.86 50454.20
## 810 811 812 813 814 815 816
## 44301.34 48114.54 52486.33 50655.55 38654.86 36957.99 44542.17
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8113
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.030356 0.7568903 3.847851
## t2* 2632.106569 167.0465235 881.751841
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.092128 5.003617 13.21763
## 2 lag_depvar 1603.496046 2656.242425 4485.05037Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Feb 24 00:50:43 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Feb 24 00:50:52 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Feb 24 00:51:01 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Feb 24 00:51:11 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Feb 24 00:51:20 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Feb 24 00:51:29 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Feb 24 00:51:38 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Feb 24 00:51:48 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Feb 24 00:51:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Feb 24 00:52:06 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 325.252 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.000 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 73.999 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 21.990 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 476.241 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2624, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2624 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.2## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.2## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.2##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-03-09 00:04:58 sería de: 26.921 pesos// Percentil 95% más alto proyectado: 35.097,94
Según TimeGPT: La proyección de la UF a 298 días más 2026-01-01 sería de: 39.556,3 pesos// Percentil 80% más alto proyectado: 39.946,4 pesos// Percentil 95% más alto proyectado: 41.030,11
Según prophet: La proyección de la UF a 298 días más 2026-01-01 sería de: 38.446 pesos// Percentil 95% más alto proyectado: 41.056
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26537.82 | 26331.66 |
| Lo.80 | 26669.99 | 26493.43 |
| Point.Forecast | 26921.46 | 26799.01 |
| Hi.80 | 31593.20 | 32074.20 |
| Hi.95 | 34386.03 | 34866.72 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5565 -0.3087
## s.e. 0.1110 0.1126
##
## sigma^2 = 37449: log likelihood = -474.19
## AIC=954.39 AICc=954.75 BIC=961.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3642 463.3959 18.3018
## s.e. 0.1004 275.1981 8.4991
##
## sigma^2 = 35487: log likelihood = -477.87
## AIC=963.74 AICc=964.34 BIC=972.85forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 700.1710 | 706.9536 | 742.9974 |
| Lo.80 | 836.1806 | 850.5687 | 836.9886 |
| Point.Forecast | 1093.1089 | 1121.8640 | 1059.2415 |
| Hi.80 | 1350.0372 | 1413.0247 | 1339.9033 |
| Hi.95 | 1486.0468 | 1567.1559 | 1517.1589 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.12
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.23.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.1 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.8.6 httr_1.4.7
## [40] readxl_1.4.3 zoo_1.8-13 stringr_1.5.1
## [43] stringi_1.8.4 DataExplorer_0.8.3 data.table_1.16.4
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.0
## [4] httr2_1.1.0 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.0.2 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.1 askpass_1.2.1 pkgbuild_1.4.6
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.11 performance_0.13.0 spatial_7.3-17
## [34] parallelly_1.42.0 codetools_0.2-20 xml2_1.3.6
## [37] tidyselect_1.2.1 ggeffects_2.2.0 farver_2.1.2
## [40] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [43] stats4_4.4.0 jsonlite_1.8.9 ellipsis_0.3.2
## [46] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.1
## [49] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [52] xfun_0.50 TTR_0.24.4 ggfortify_0.4.17
## [55] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [58] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [61] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [64] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [67] networkD3_0.4 gtools_3.9.5 generics_0.1.3
## [70] htmlwidgets_1.6.4 ggstats_0.8.0 pkgconfig_2.0.3
## [73] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [82] knitr_1.49 rstudioapi_0.17.1 tzdb_0.4.0
## [85] uuid_1.2-1 nlme_3.1-164 curl_6.2.0
## [88] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [91] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [94] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [97] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [100] evaluate_1.0.3 mvtnorm_1.3-3 cli_3.6.4
## [103] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [106] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [109] rstan_2.32.6 QuickJSR_1.5.1 viridisLite_0.4.2
## [112] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.26 bit_4.5.0.1#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))